Abstract
Important advances in the understanding of "random" processes have produced a variety of stochastic algorithms that offer unprecedented scope and utility in the study of physical systems. These algorithms represent a departure from the usual philosophy inherent in the study of many-body problems and have a number of significant features. Chief among these features are simplicity, weak dependence on dimensionality, and ease of transition between classical and quantum-mechanical descriptions. These methods are also readily adapted for use on massively paraliel computer architectures. These new stochastic methods represent a valuable addition to the tools available for the analysis of both equilibrium and time-dependent many-body problems.
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